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Online age verification: a new era

Online age verification: a new era

For years, businesses delivering age-restricted services online have gotten away with hiding behind a tick box. But it seems that governments are waking up to the dangers caused by exposing minors to age-restricted content. New legislation is starting to enforce more stringent online age verification measures across social media, dating, gaming and adult content sites. But what does this mean for the operators of those sites? How do they verify a user’s age without adding friction to their carefully crafted user experience? In a world where 40% of users are said to abandon a website if it takes more than 3 seconds to load, how do you stay competitive and protect your users with proper age-gating?   How have businesses done online age verification in the past? The Age Verification Providers Association (AVPA) said it best: “Age verification requires more than just checking a box or even typing in your date of birth to claim you are old enough to access an online service.” And up until recently, these methods of online age verification were standard practice. Then, credit card verification and electoral roll checks came along to add an extra layer of assurance for businesses.  However, these checks were still easy to bypass. Children could still access content that is considered harmful – and adults were still able to enter spaces designed for children. There was a clear need for change and more sophisticated technology.   New ways to age gate your website The most common way businesses digitally age check their customers nowadays is by asking them to upload an ID document and capture a selfie. Optical character recognition (OCR) is used to extract the data and face match technology matches their real-time selfie with their document photo. More advanced providers can offer a higher level of age assurance with Near Field Communication (NFC) technology. This instantly verifies an ePassport to the same high level as in the airport. The biggest breakthrough in age tech, though, is facial age estimation. This determines a person’s age by analysing their face from a live or uploaded image. It uses a combination of artificial intelligence and machine learning. This is one of the only methods that doesn’t require you to have a document to hand, like a passport or credit card. And, it’s also completely anonymous.   Personalised age checks At Yoti, we’ve developed an array of sophisticated methods that let businesses gain the right level of assurance that someone is the appropriate age to be accessing your services, which varies across industries. Being able to customise the way they do online age verification ensures they are hitting the right sweet spot between assurance and friction. This could range from an estimated age based on a real-time selfie or a verified date-of-birth from an ID document. Once the age check is complete, we share an Over or Under age and delete all data captured during the process. Whether your priority is speed, age assurance or accessibility, you can pick and choose the tools that work best for you and your users.   Age assurance tools Facial age estimation: Your users just have to look at the camera on their device and take a selfie. Our world-class technology will calculate an estimated age based on facial features within a year or two accuracy. Yoti app: With over 10 million Yoti app downloads, you could make the most of the existing network of users that have already verified their age with us by uploading their ID document. They just scan a QR code on your website and prove they’re over your age threshold using their phone.  ID verification: Your users can prove their age by scanning their ID documents and taking a biometric selfie on their device. We use leading AI and NFC technology to verify the user and their ID document, with our identity experts on standby for when your users take scans or selfies that need a trained human eye. For example, if they take a selfie in a dark room.  Credit card check: You users input their credit card details, and we do an active card check to make sure it’s valid. Database check: We verify your users’ name, date of birth and address against a database. Mobile provider check: We verify your customer is over 18 using their mobile provider details.   Why we designed it that way One of our seven ethical principles is to enable privacy and anonymity, so being GDPR-aligned is as important to us as it is to you and your business. We built our age verification with Privacy by Design to empower your users to keep their data safe and private. We also limit the amount of data your users need to share with you and with us. And the individual data we do have – such as date of birth or full name – is securely encrypted and held separately. Once we share with you whether your users are over or under your threshold, we instantly delete their data. This way, your users can prove their age whilst remaining entirely anonymous. When their age is shared, you will only know if they pass the threshold you have set out. For example, if you only want over 18s to access your site, that’s all you’ll know when users complete an age check. We also only use first-party cookies when helping people prove their age. This way, businesses can be confident that they can verify their users’ age without invasive privacy practices.   Future-proof your business against new legislation As digital spaces develop, online businesses will need to have measures in place to protect themselves from legislation that could impact their operations. A UK Government white paper published in 2020 called for companies to start developing and sharing technological solutions to help protect children from online harms, rather than complying with minimum requirements. So, over the next few years, we expect a number of legal frameworks will disrupt online services that are used by children. The newest set of standards have been set out by the ICO in the Age Appropriate Design Code (the Code), which came into effect on 2nd September 2021. It is one legal framework that aims to create a safe digital space for children. We are working hard to ensure that we comply with the Code, but also we want to encourage other companies to start taking measures to help protect the children who use their services.  The International Organization of Standardization (ISO) is also drafting their own Age Assurance Systems Standards, so no matter where you do business, it may be time to age-gate certain parts of your website. In any case, we want to empower you to do the safest thing for your users, especially children. If you would like to learn more about how your business can benefit from the newest age verification technology, get in touch with us today.

Age verification: the solution to buying age-restricted products in supermarkets

Age verification: the solution to buying age-restricted products in supermarkets

Supermarkets as we know them today are vastly different from the ones we were used to twenty years ago. Doing the weekly shop is quicker and easier thanks to supermarkets implementing technological solutions like scan-as-you-shop and self-checkout terminals. Shoppers are becoming increasingly independent, but how can supermarkets assure that their customers are old enough to buy age-restricted products with minimal staff intervention and manual ID checks? Across the UK, supermarkets are looking to adopt new ways to check that their customers are old enough to buy age-restricted products. By embedding our leading age verification technology into their self-checkout terminals, supermarkets could reduce the amount of time customers spend scanning and paying for their shopping.    Digitise your age checks Age estimation Our age estimation tech can instantly estimate a customer’s age via facial analysis – no ID document or pre-registration needed. How it works Customers simply look at the camera on the self-checkout and have their age estimated based on their facial biometrics. If the result of the estimation is above the threshold set by the supermarket, then the customer can continue to pay for their items. If the result of the estimation is below the threshold, the customer can use their Yoti digital ID to prove their age.   Yoti app  The Yoti app could allow shoppers to prove they’re over a business’ age policy using a digital ID on their phone. This requires downloading the free Yoti app and verifying themselves with an ID document and a biometric selfie. They just need to do this once and can use their digital ID for life. How it works Customers scan the QR code on the self-checkout screen using their Yoti app.  They consent to sharing a verified ‘Over Age’ in their Yoti app, which will tell the business they’re over the required threshold.  See how it works Make life easier… … for staff: Efficient: ID checks account for 50% of staff interventions at self-checkouts. Our innovative age verification technology would empower customers to prove their age independently of staff, who can focus on other tasks in the supermarket. Safe: Year-on-year, age-restricted sales are an overwhelming trigger for abuse and violence towards supermarket staff. Age verification tech could reduce contact between staff and shoppers, removing some of the friction created by manual checks. Accurate: Our age estimation technology is more accurate at detecting age than humans. There are several reasons why an employee might not accurately complete age checks. For example, tiredness, biases, or falling prey to fake IDs. This takes the pressure off human error and helps businesses avoid hefty fines for selling age-restricted goods to minors. For more information, read our white paper. … and customers: Faster: Age checks can be done in real-time with accurate age estimations taking less than 5 seconds, dramatically reducing the time spent at checkouts. Accessible: Studies show that about 4.5 million adults in the UK do not own an in-date, recognisable ID. Age estimation would allow them to prove their age without carrying a physical ID card. More convenient: We’re rarely without our phones, or our face for that matter. Shoppers would no longer be denied a sale if they forget their ID document, nor would they risk losing their valuable ID documents, like a passport or driving license. See what these customers thought of our age estimation solution at the self-checkout. If you’re a retailer looking to make age checks more efficient, contact us today or click here for more information.

Be the company people trust: why you need to prioritise data privacy

Be the company people trust: why you need to prioritise data privacy

It’s not just your consumer’s responsibility to protect their personal data, but yours too. Everyone is in on the conversation of data privacy; if you don’t make it a priority, then you risk losing your consumer’s trust. With the rise of online services and the ever-growing move to a digital world, consumer concerns over data privacy and how businesses handle their information have risen.  People are no longer comfortable sharing their personal information now that they’re more aware of its value and potential misuse. A McKinsey survey discovered that 87 per cent of consumers say they would not do business with a company if they had concerns about its security practices. In comparison, 71 per cent said they would stop doing business with a company if it gave away sensitive data without permission. The need for transparency into how data is collected, shared and used is in hot focus for businesses to be more accountable—likewise, consumers need more control over data consent. In the last few years, governments have become more involved in regulating data collection and protecting consumers’ privacy rights. Regulatory restrictions like the EU’s GDPR and recently the state of California’s CCPA  provide more updated frameworks aimed at protecting consumer data. Being compliant with an ever-changing regulator landscape while maintaining trust with your consumers is challenging. In comparison to the damage, mishandling of personal data can be a costly reputational fallout. The opportunities beyond being compliant have far greater value for your business. Data is valuable, which makes it even more important to protect that data and ensure privacy. Investing in a privacy policy is advantageous to maintaining and improving the value of your brand. We’ll discuss why you need to prioritise data protection, your consumer’s concerns about data privacy and how we can help you protect their data. Let’s look at why you need to prioritise data protection and consumers’ privacy to build better trust.    Seeing the advantages of making data protection a priority Being transparent gives you a competitive advantage Every company understands the importance of consumer data, but not many seem to understand prioritising how they collect, share and use personal data is just as important. Instead, it is treated as a checkbox exercise to meet industry regulatory requirements and show compliance.  Data protection shouldn’t be treated as just a checkbox exercise. There is clear business value in having a comprehensive privacy policy and being transparent with consumers about how you handle their data. Deloitte’s report into data privacy as a strategic priority points out that “[i]t can also help the business use its superior data privacy capabilities as a strategic differentiator in an increasingly digital and competitive marketplace.” Your business’s ability to remain competitive with industry peers is crucial to its survival. Of the companies that took part in a Cisco survey, 97 per cent stated they had seen benefits like a competitive advantage or investor appeal from investing in privacy.    Protecting personal data supports innovation  Elizabeth Denham, Information Commissioner at the ICO, in her keynote speech at the data and the future of financial services 2021 conference, reminds us that, “[a]t its core, data protection is about trust. It has always been about trust. Data protection law was born in the 1970s out of a concern that the potential from emerging technology would be lost if we didn’t embrace innovation. That link with trust remains today. The data-driven innovations you are planning will only work if people are willing to share their data with you, trusting it will be used fairly.” Many still see privacy as something that gets in the way of innovation, but the opposite is proven to be true. If you’re in the business of providing products/solutions/services that follow a customer-first approach, then consumer trust is central to your brand.  Columbus CEO Thomas Honore writing for MicroscopeUK, highlights that “[d]ata is your most powerful tool for driving disruption and innovation, not only by providing insights but also by giving you direction.” A proactive approach to data protection is key to maintaining trust. It is a worthwhile investment that many industry leaders understand the value of prioritising data privacy. Forty-two per cent of companies surveyed for a Cisco report said that investing in privacy has enabled agility and innovation in their organisations.   Responsible data protection builds trust and understanding Gathering personal data is valuable and helps your business better connect with the ideal consumer, as it allows you to understand your consumers. Having strong security controls and being upfront with consumers about how you protect their data will build trust. PWC research on Americans and privacy found that 81 per cent of surveyed consumers say the potential risks they face from data collection by companies outweigh the benefits. Additionally, 84 per cent of consumers for a Salesforce survey said they are more loyal to companies with strong security controls.   What concerns do your customers have? Consumers are demanding more be done particularly in the way their information is used, handled and shared or in some cases without their knowledge. Trusting companies with their personal data is now a source of hesitation for a lot of consumers.  They are most concerned with:   The rise of data breaches  Data breaches are a consumer’s top concern about the handling of their personal data. Their fears are not unfounded. Over the last few years in America alone, the RSA reported that 45 per cent of consumers had their personal information compromised by a data breach.   A lack of control Consumers are much more aware of how valuable their personal data is to a business. As a result, they are now more conscious about who they share it with and how much. In the PwC research into Americans and Privacy, 81 per cent of consumers feel they have little control over data collected about them by companies while only 10 per cent of consumers feel they have total control over their personal information.  Therefore, prioritising privacy and implementing data protection processes and procedures is vital on both sides. It upholds an honest approach that meets your consumers’ data management expectations. Likewise, being upfront by informing them about why and what data you’re requesting will empower them to make an informed decision whether to share.   How Yoti can help build trust on both sides Businesses are required to collect certain information about their consumers to be compliant. As such, your consumers have to feel secure when sharing personal data. That is why we build our solutions with privacy in mind; Encouraging personal data ownership is a core ethic of our seven ethical principles. We give consumers a privacy-friendly way of verifying their identity details.  Our Digital ID app helps your consumers share only the details you need and nothing more. As privacy is a priority, our Digital ID app helps your business protect personal data. This is done by us ensuring to: Encrypt your consumer’s personal data separately to protect against privacy breaches and cyber attacks. We split up their personal data, encrypt it and securely store it on our databases.   Only allow the user access to their encrypted details. The key to unlocking it is all stored safely on their phone. They are the only ones who can unlock the app with a five-digit PIN or biometric. We call this activating the master key. This master key is stored on your phone and is the only way of pulling together your attributes and turning them into readable text. Yoti also encrypts your master key for extra security.   Request what details you need. Consumers only share specific information rather than all of their data. Like you, the app gives businesses the ability only to request what personal information you need from them and is a sure way to build trust on both sides.   To make sure we’re accountable and continue to build solutions with privacy and security at the centre, we take regular guided advice from our Guardian Council, an independent board of expert professionals and dedicated advisors from data privacy, human rights, online harms and last-mile technology sectors.       Our digital footprints will continue to grow the more we embrace technology. While many consumers are getting to grips with it and aim to take control of their personal data, keeping their data secure is at the heart of a customer-first approach. The better we are at protecting our consumer’s personal information, the easier it will be to share it with trust securely.

How AI and machine learning can help build better AML programs

How AI and machine learning can help build better AML programs

As financial institutions move towards FATF-recommended risk-based AML programs, artificial intelligence (AI) and machine learning can help them be more effective and efficient in the fight against financial crime.  The global framework for fighting financial crime white paper by The Institute of International Finance and Deloitte LLP highlights that: There is growing consensus that the current global framework for fighting financial crime is not as effective as it could be and that more needs to be done at the international, regional and national levels to help identify and stem the flow of illicit finance – an activity which supports some of the worst problems confronting society today, including terrorism, sexual exploitation, modern slavery, wildlife poaching and drug smuggling. The impact has rippling effects; AML professionals, governments and regulator bodies globally are discussing better approaches to preventing and reducing money laundering from entering financial systems. The most critical area of address is the AML programs institutions must implement within their systems to be compliant.  However, it is challenging for institutions to reform their AML programs. The Money Laundering Bulletin points out that “some of the key problems, and therein some of the most promising solutions, lie with the technology used by companies to underpin their AML programmes. Most of the systems used today originate from the 2000s and were never built for the scale of data or demand we see now.”  In the fight to improve their approaches to identify and reduce money laundering, regulatory bodies and industry leaders acknowledge and encourage the use of Artificial Intelligence (AI) and machine learning to comply with AML regulations. AI and machine learning are proving helpful to implement within anti-money laundering programs to be more effective and meet ever-evolving compliance obligations. In this article, we will take a look at how AI and machine learning can increase both the efficiency and effectiveness of Anti-money laundering programs.   Why AI and machine learning is important in AML programs AML programs require compliance teams to gather intensive amounts of customer data as part of the first line of defence during customer due diligence (CDD) processes and ongoing transaction monitoring. This process was previously a manual and repetitive task to analyse if customers present a potential risk.  Here is how AI and machine learning can be applied in different stages of the CDD and ongoing transaction monitoring processes.    AI and machine learning can assist during client onboarding  For AML compliance teams to be compliant, they must carry out Know Your Customer (KYC) processes. They are required to analyse large amounts of data from a range of external sources: adverse information, media, Politically Exposed Persons (PEP), sanctions, and watchlists. To effectively screen an individual, they must gather quality data to assess and know the customers are who they say they are.  Building a better assessment and profile of customers involves being able to match that individual to their identity. It is just as essential to reduce the risk of identity fraud, particularly synthetic identity theft, which is one of the most prominent threats institutions face and is one of the most difficult financial crimes to detect. Furthermore, Information experts Thomson Reuters explains, “financial institutions often struggle with synthetic identity fraud as well. That’s because fraudsters often open accounts with synthetic IDs to establish credit and behave as stellar customers for months or even years before they use those accounts to defraud financial institutions.”  AI and machine learning can help AML compliance teams verify the identity of an individual in real-time. With biometric face matching, the customer’s identity is scanned and compared with a selfie against a photograph of the customer’s ID. Together both the selfie and ID are assessed in real-time to detect liveness and potential alterations that lead to signs of a fraudulent document.   Ultimately, AI and machine learning aid by speeding up the onboarding experience with a more streamlined onboarding process and provides a more robust first layer in KYC processes to show you have done your CDD checks.    AI and machine learning aids in ongoing transaction monitoring Money launderers aim to move illicit funds through financial systems as quickly as possible in order to go undetected. These programs are required to conduct continuous monitoring of customers’ transactions to spot potential suspicious behaviour that needs further investigation.  AI can improve the way traditional programs and compliance teams review and source data to build risk profiles on individual customers and improve the recognition of if a customer presents a risk. It scans large data sets at scale, provides categorised breakdowns of profiles, and connects to real-time databases, so compliance officers are alerted as soon as there’s any update in risk status.  In our previous article, regulatory expectations are evolving: how robust is your AML program discussing the challenges slowing progress, we looked at the challenge false positives present. Older programs are known to typically produce false positives that are costly and time-consuming to investigate.  AI can assess while learning from customer data to detect and recognise suspicious behaviour. The ability to better spot any potential risk a customer may present helps AML compliance teams create a more in-depth risk profile. Using machine learning within the transaction monitoring process can increase the scale at which AML compliance teams screen customers’ behaviour and detect risk. The technologies can analyse large quantities of data and further aid compliance officers in ongoing transaction monitoring by more accurately indicating risk. Additionally, AI has the capability to spot known risks about an individual and make the research process more efficient by improving the time taken. According to the Money Laundering Bulletin, “applying AI to the creation of AML risk data can prevent compliance breaches by spotting previously unknown risks, updating entities faster, identifying remote linkages between entities and enhancing existing profiles with more information (sic) to help make better decisions more rapidly.” AI and machine learning speed up the process with an analysis of larger sizes of data sets and improved categorised breakdowns of results. AML compliance teams are able to spot suspicious activity quickly, assess the level of threat based on accurate data, review comprehensive customer risk profiles and effectively investigate.   Improve the power of AML professionals decision making AI and machine learning are not only improving a once static system that struggled to effectively and efficiently screen, onboard and monitor customers. These technologies are helping institutions provide a better customer experience by streamlining the onboarding process and accurately identifying high risk.  With the added benefit of better data, AML compliance teams can make better decisions. They can build a robust risk-based AML program by combining their human expertise with AI and machine learning in the battle of preventing their financial systems from being used for money laundering. If you would like to learn about how we make compliance simpler, head over to our Identity Verification solution. 

Identity-driven eSignatures [product sheet]

Identity-driven eSignatures [product sheet]

Know exactly who’s signing your documents with legally-binding electronic signatures linked to a verified digital ID. Simply upload your documents, place the signature boxes and request recipients sign with the Yoti app. They’ll receive an email with the documents and a QR code to scan with their Yoti Digital ID. They consent to sharing their verified identity details in their app and you can achieve eSignatures and KYC in one simple swoop. Download your product sheet

Regulatory expectations are evolving: How robust is your AML program?

Regulatory expectations are evolving: How robust is your AML program?

Managing financial crime is very complex and presents several challenges for financial institutions to develop robust anti-money laundering (AML) programs and meet compliance requirements. AML compliance officers understand the importance of keeping one step ahead of criminals trying to find weak points in their financial systems to exploit. Continuous discussions by industry leaders at the forefront of combating financial crimes agree that more needs to be done sooner to detect and prevent money launderers from misusing systems. Thought leader Kevin Buehler from McKinsey wisely points out that “the stakes in this fight have never been higher for financial institutions. Money launderers are using increasingly sophisticated methods to avoid detection, and regulators are pressing for improved efficacy in anti-money laundering (AML) programs.”  However, it is proving a more significant challenge to keep up with the new inventive ways criminals are testing their AML systems while ensuring they comply with AML regulations. In this article, we’ll examine the challenges anti-money laundering professionals face when making a more robust AML program to combat the increase in financial crime.    What challenges are slowing the progress of more effective and efficient AML programs?  It is widely known that financial institutions are undergoing vast digital transformations to remain competitive and innovative with the types of financial services provided to customers. Legacy systems are one aspect slowing their progress to adopt innovations and digitalise internal processes. Inherently, legacy systems affect AML programs’ potential to detect and identify the source of money laundering or the ability to prevent it.  Legacy systems are one of the several major challenges slowing the prioritisation of an effective and efficient AML program. Here are four of the significant difficulties further slowing change.  1. Regulatory pressures and the cost of compliance   Anti-money laundering professionals all believe in the need for regulations to help prevent the misuse of their financial institution’s services. It is challenging to ensure that AML programs sufficiently meet the increasing volume and complexity of regulations. In addition, institutions are estimated to spend billions each year in combatting financial crime. A survey commissioned by Refinitiv, one of the world’s largest financial markets data and infrastructure providers, reported that 3.1 per cent of annual turnover is spent combating financial crime, representing a sum of $1.28 trillion for organisations surveyed from 19 countries.  Conversely, failure to comply can result in hefty fines. According to Global Investigations Review, enforcement actions and penalties for non-compliance with AML regulations continue to increase. They state that globally, there were 58 AML penalties in 2019 at a total of US$8.14 billion, compared to 29 penalties totalling US$4.27 billion in 2018. In 2020, global AML fines for financial institutions increased again to more than US$10.3 billion. As this shift occurs, financial institutions are further challenged to meet stringent regulatory and compliance requirements while remaining fiercely competitive and providing a trusted service.    2. Expensive false positives not detecting authentic illicit activities Financial institutions must have many layers of defence for their AML programs to detect potential illicit activities. Customer Due Diligence (CDD) is at the centre of an effective AML program. Magazine ACAMS Today highlights that, “banks need to conduct due diligence on business operations, industries, customer characteristics and regions, in order to obtain adequate, complete and truthful customer information as the basis of analyses.”  The second is continued monitoring and screening of those customers and their transaction habits to identify any illicit transactions. Despite institutions setting these measures in their systems, the current controls in place to monitor customer behaviour are not evolved enough and, in some cases, are too sensitive in their detection of potential illicit activity.  This leads to innocent customers incorrectly being flagged as performing suspicious activities. As a result, institutions face costly false positives yearly and struggle to reduce those. Reportedly on average, 55 per cent of ‘false positives’ and inefficiencies can be eradicated by the most modern systems, accounting for 42 per cent of institutions’ AML costs. That equates to £2.7bn.   3. Inability to detect certain criminal activities   In 2020, the EU expanded their AML regulations to include more offences that fall under money laundering. The EU sixth anti-money laundering directive (6AMLD) now requires institutions to acquire data to meet new transaction monitoring that can better spot money gained from human trafficking.  This presents a challenge for AML compliance officers who, according to BusinessWire, admitted to having to report and investigate criminal financial activity linked to human trafficking. Almost three-quarters (75 per cent) aren’t confident in their ability to identify human trafficking signs amongst transactions. It is becoming hard for institutions to keep up with newer criminal methods and the use of technology to go undetected. Criminals continue to take advantage of the loopholes in regulations and AML programs.    4. Institutions left vulnerable to crime during the pandemic  During the most challenging moments of the pandemic, many industries, particularly financial institutions, had to close their physical stores and rapidly adapt their systems to deal with the increased use of digital services. Unsurprisingly, there was an increase in fraud and cybercrimes. The Financial Action Task Force (FATF)  COVID-19-related Money Laundering and Terrorist Financing report state that, “the increase in COVID-19-related crimes, such as fraud, cybercrime, misdirection or exploitation of government funds or international financial assistance, is creating new sources of proceeds for illicit actors.”   Institutions challenged to improve older AML programs are faced with an increase in smarter criminal activity and advanced attacks.    AI and machine learning improving AML programs Regulators are encouraging financial institutions to move towards a risk-based approach faster and encourage the use of AI and machine learning.  For instance, in 2020, on the gathering of experts from the Spanish and European banking sector, Global Head of Supervisors, Regulation & Compliance at BBVA, Eduardo Arbizu pinpointed that technical challenges facing institutions must improve in the fight against money laundering. Arbizu explained, “[w]e must leverage technology, especially artificial intelligence and big data, in our anti-money laundering efforts. There is a long way to go; there are still legal hurdles we need to overcome, but, undoubtedly, we have to rely on technological solutions that help us improve”.  As we lean more towards the ever-increasing use of data, conventional AML programs cannot keep up with criminals using even more sophisticated methods. To allow AML compliance officers to effectively and efficiently future proof their institutions, artificial intelligence (AI) and machine learning can help in the fight against financial crime.   If you would like to learn more about how AI and machine learning can help build better AML programs, you can read more about it on our blog.